This CRCNS application derives from work performed in a current DAAD (Deutscher Akademischer Austausch Dienst, German Academic Exchange Service) Grant between the Technical University of Berlin and Penn State University entitled: "Feedback control of spreading depolarizations in neural systems: Theory and Experiments". The design of this CRCNS proposal, and all preliminary data, were generated during the course of German Faculty and PhD students coming to Penn State University, and the synergistic collaborative efforts to establish the feasibility of feedback control of spreading depression. Spreading depression (SD) is a dramatic depolarization of brain that propagates slowly and is the physiological underpinning of the initial aura in migraines. The following hypothesis is posed: SD can be represented in computational models of the underlying neuronal biophysics, and can therefore be controlled using model-based control strategies. The project starts by developing an experimental preparation using a tangential 2-dimensional visual cortex rodent brain slice. SD is triggered with a perfusate potassium perturbation, and SD is imaged using a sensitive CCD camera that detects the intrinsic optical imaging signal associated with index of refraction changes from cellular swelling. A model-based strategy similar to that used in autonomous robotics such as airframe autolanders is employed. A hardware and software control system takes the optical image in real-time, fuses it with a model of SD, reconstructs the underlying physiological processes, calculates needed control, and modulates an electrical field to modulate SD. Both biophysically accurate models of the neuronal compartments and ion flows, and reduced models that reflect the dynamics of the wave propagation, will be used as observation and control models. Intellectual merit: This will be the first experimental demonstration of model-based control of a neuronal network. Similar engineering strategies have revolutionized advanced robotics, and the fundamentals learned from a fusion of computational neuroscience with control engineering will have wide ranging adaptations in other areas of neuronal modulation. Furthermore, this will be the first model-based control of a physiological mechanism that underlies a dynamical disease of the brain - migraine auras. The control models will further serve as probes to gain increased understanding of the mechanisms of SD. The team assembled has a substantial track record in the range of disciplines required to carry out this project: neurophysiology, experimental and theoretical physics, computational neuroscience, control theory and neural engineering. The preliminary work shown in the proposal suggests that this project is feasible given the resources requested. Broader impact: Fusing computational neuroscience models with modern model-based control theory will lay the foundation for a transformational paradigm for the observation of activity within the brain, as well as access to a more optimal technology for the control of pathological processes in the brain. A transdisciplinary German-American educational collaboration will be formed where the graduate students trained (and the PIs) will synergistically work together within the interface between computational neuroscience, control theory, experimental neurophysiology, and control system engineering. The PIs have a track record in training and mentoring women and underrepresented minorities, and they will make every effort to seek such trainees for the mentoring opportunities of this project. As a collaborative partnership, the PIs anticipate that what is learned in controlling SD may provide a set of testable strategies for electrical control of migraines in people who suffer from severe migraine attacks and are pharmacologically intractable. Furthermore, based upon this CRCNS, the same science and engineering will be applicable to the modulation of oscillatory waves and rhythms in both in vitro (e.g. Schiff et al 2007) and in vivo (e.g. Sunderam et al 2009) systems. They plan to widely disseminate the algorithms and hardware design developed as described in the Data Management Plan.